1. Field of the Invention
The present invention relates to a method and system for measuring and reporting time based parameters associated with heart activity. More particularly, the present invention relates to a cardiography system and method using automated recognition of hemodynamic parameters and waveform attributes for monitoring and recording signals derived from heart valve activity and guiding goal-directed therapy by correlating cardiovasculograms (CVG) with waveform and hemodynamic data stored in local memory.
2. Discussion of the Related Art
Cardiac output and circulatory flow are a balance of the pumping ability of the heart. Congestive heart failure (CHF) is a condition in which the cardiac output is unable to meet the metabolic demands of the body. This condition can vary in severity from a simple elevation in cardiac filling pressures, known as compensated failure, to severe hypoxia and edema, known as decompensated failure.
CHF is thought to result from a failure in the contractile elements of the heart during the systolic phase of the cardiac cycle, which is known as systolic congestive heart failure. Systolic CHF is characterized clinically by an ejection fraction of less than 30%. Systolic CHF can be a result of a myriad of possible pathologies affecting the contractile ability of the heart muscle including myocardial infarction, cardiomyopathies, and metabolic disorders. Management of this disorder has evolved over recent years and is highly dependent upon the severity of the condition. Most treatment regimens involve attempting to increase systolic contractility or focusing on hemodynamic manipulations that allow the heart to take a passive role in circulatory control.
More recently it has been recognized that limitations in cardiac filling and venous return during diastole can also result in an abnormal circulatory flow, which is known as diastolic congestive heart failure. Diastolic CHF is defined as the condition in which there is evidence of the clinical signs of CHF in the presence of normal systolic functioning. This condition occurs in as many as 30% of patients presenting with heart failure. Some of this dysfunction may be due to a stiff myocardium limiting the passive phase of diastolic filling. However, the majority of dysfunction is caused by lengthening of isovolumic myocardial relaxation or isovolumic relaxation times (IVRT). Myocardial relaxation is an energy-dependent, active process that is mainly unconstrained by preload and afterload considerations. Ventricular hypertrophy is often the end result of long-standing hypertension and is commonly responsible for delays in IVRT due to abnormalities in calcium kinetics. Researchers have shown that an IVRT greater than 0.125 sec is indicative of diastolic dysfunction. Patients presenting with CHF due to diastolic dysfunction may not respond to traditional therapies. These traditional therapies can even be detrimental to patients presenting with CHF due to diastolic dysfunction. Patients with evidence of acute decompensation secondary to a diastolic mechanism may have worsening of symptoms, hypotensive response, and reduced cardiac output with the typical off-loading treatments of diuretics or preload reducing medication. As a result, it is important to identify accurately which type of CHF a patient is presenting, in order to identify appropriate goal directed therapies.
The analysis of waveforms obtained from physiologic monitoring is a common practice in medicine. Clinicians have used waveform patterns obtained from electrocardiography, capnography, cardiotocography, and spirometry to assist in the diagnostic assessment of patient pathology. The unassisted, human interpretation of CVG pattern recognition and differentiation of these waveforms is a clinical art form that requires experience and skilled expertise. However, automated computerized interpretations of waveforms based upon specific segmental waveform criteria have been widely used in medicine to assist clinicians in the diagnostic process. In the field of electrocardiography, the interpretative waveform criteria have been developed based upon evidence from clinical correlations and standardized for specific diagnoses. Proprietary computerized algorithms use these criteria for their electrocardiographic interpretation.
Clinical evidence supports the use of waveform analysis and diagnostic interpretation in the field of impedance cardiography (ICG). ICG is a technique used to provide non-invasive monitoring and analysis of a patient's cardiac performance. ICG systems measure and report several time-based parameters related to cardiac performance, including the pre-ejection period (PEP) and the left ventricular ejection time (LVET). ICG systems produce ICG signals from monitoring movement and volume of blood as a result of the heart contracting. Exemplary ICG systems are shown and described in Ackmann et al., U.S. Pat. No. 5,178,154; and Reining, U.S. Pat. No. 5,505,209 both incorporated by reference herein in their entireties. The '154 and '209 patents disclose the use of electrode bands placed on a patient with high frequency, low magnitude electrical current applied to the electrode bands. Voltage changes across the bands are read, filtered and converted into thoracic impedance. The ICG system displays the thoracic impedance signal versus time to create a visual display of the ICG signal. The '154 patent further discloses that ICG systems can receive conventional electrocardiograph signals, signals from blood pressure monitors, signals from piezoelectric microphones attached to the chest of the patient and the like. These signals, in addition to thoracic impedance, can be stored and averaged via a memory storage device connected to the ICG system.
A CVG is a waveform produced by the processing of impedance cardiography (ICG) signals and which may also be supported by processing other signal inputs such as electrocardiography (ECG) signals, phonocardiography (PCG) signals and other hemodynamic signals. The CVG waveform in combination with the accompanying electrocardiograph, describe the electromechanical events of the cardiac cycle. The CVG is a signature waveform and can be interpreted by physicians in much the same way as electrocardiograms are interpreted. Despite improvements in ICG systems and/or signal processing, there have been no advances in the methodology of automated waveform analysis for ICG systems. Specifically, there exists a need for using CVG waveforms in an automated system to differentiate decompensated heart failure from other common clinical conditions and to further distinguish between diastolic and systolic forms of heart failure.
Phonocardiography (PCG) is a non-invasive technique used by healthcare professionals to monitor cardiac performance. PCG systems generate PCG signals by monitoring the opening and closing of valves within a patient's heart. PCG systems use a microphone that records sounds of heart valve activity, similar to electronic stethoscopes known in the art, in order to provide indications of aortic heart valve opening (shown as S1 on FIG. 1) and aortic heart valve closure (shown as S2 on FIG. 1).
Another non-invasive system used to monitor heart activity is an electrocardiogram (ECG) system. ECG signals are electrical signals that are generated from the depolarization and repolarization of myocardial cells in a patient's heart. ECG systems are known to include a first external electrode attached to a patient's skin, a second external electrode attached to a patient's skin and optionally a third external electrode attached to a patient's skin. An amplifier is used to monitor electrical heart activity signals at the first and second electrodes and generate an ECG signal based on the difference between these activity signals. The optional third electrode can be used to reduce or offset noise in the ECG signal.
Still another non-invasive system used by healthcare professionals to monitor cardiac performance is a blood pressure system. A patient's blood pressure is monitored according to known techniques and converted into a blood pressure signal. The blood pressure signal is then displayed on a blood pressure waveform. Blood pressure waveforms, similar to PCG waveforms, can be used by healthcare professionals to identify heart valve closure because the dicrotic notch in blood pressure waveforms reflects closure of the aortic heart valve. Other exemplary systems using signals that have pulsatile characteristics resulting from the contraction of the heart are shown and described in Kimball et al., U.S. Pat. No. 6,763,256, herein incorporated by reference in its entirety.
The PEP is defined as the period of isovolumic ventricular contraction when the patient's heart is pumping against the closed aortic valve. In ICG systems, the PEP is measured starting with the initiation of the QRS complex (the “Q” point on FIG. 1) of the ECG signal and ending with the start of the mechanical systole as marked by the initial deflection of the systolic waveform (the “B” point on FIG. 1) of the ICG signal coincident with the opening of the aortic valve or the onset of left ventricular ejection into the aorta. The LVET begins at the end of the PEP and ends at the closure of the aortic valve (the “X” Point on FIG. 1) when ejections ends.
It is important that ICG systems provide accurate results for the PEP and the LVET because healthcare professionals utilize the results of these parameters when making decisions about patient diagnosis and care. Additionally, accurate determination of the PEP and the LVET time intervals is also required for accurate and reliable determination of subsequent and dependent parameters. For example, results from determination of the PEP and the LVET are used to calculate the systolic time ratio (STR), where STR=PEP/LVET. While many ICG systems use proprietary equations for determination of stroke volume (SV), it is commonly known that SV equations frequently incorporate LVET as an input parameter. Accordingly, accurate determination of time intervals between the PEP and the LVET is also necessary for accurate determination of SV, and subsequently for cardiac output (CO) based on SV and heart rate (HR), where CO=SV*HR.
Many CVG waveforms, particularly for healthy individuals, provide sufficient detail so that ICG systems can identify the location of the aortic valve opening and closing, or the LVET, with a high degree of confidence. For example, in the CVG waveform depicted in FIG. 1, opening, B point, of the aortic valve and closing, X point, of the aortic valve are easily identifiable. When comparing the CVG waveform with the phonocardiograph (PCG) waveform (both shown in FIG. 1), marking of the B point in the CVG waveform is confirmed by the time-associated presence of the S1 component in the PCG waveform. Similarly, marking of the X point in the CVG waveform is confirmed by the time associated presence of the S2 component in the PCG waveform.
A number of parameters, including but not limited to cardiac output, thoracic fluid content, Heather Index, and the like, have been derived from impedance signals to assist in the diagnosis of decompensated heart failure. Traditionally, however, ICG systems only analyze attributes of the impedance signal when determining the location of heart valve activity. Some ICG systems may record and display PCG signals, blood pressure signals, and/or other signals having pulsatile characteristics resulting from contraction of the heart, but these ICG systems do not integrate these signals into the automatic location of heart valve activity. ICG systems alone often lack sufficient information to accurately and reliably determine the PEP and the LVET because of confounding information related to opening and closing of the patient's aortic valve. For example, in the CVG waveform depicted in FIG. 2, closure, X point, of the aortic valve could be any of several depressions following the peak blood flow, C. The known algorithm selected the deepest depression in the CVG waveform because the aortic valve closure is often thought to produce the strongest negative signal. However, when the CVG waveform depicted in FIG. 2 is compared with the PCG waveform depicted in FIG. 2, the aortic valve closure, X point, should have been one of the later depressions in the CVG waveform in order to correlate with the time associated presence of the S2 component in the PCG waveform. Accordingly, there is a need for an impedance cardiography method and system for automated correlation of impedance signals from ICG systems with other signals derived from heart valve activity in order to provide more accurate identification of heart valve activity.
Many of the specific segmental criteria used in this comprehensive pattern recognition are based upon well-established characterizations of changes in systolic and diastolic function as determined from elements of the impedance cardiogram.
It is known that experienced healthcare professionals can recognize, or diagnose, certain disease states by analyzing hemodynamic parameters in combination with visual displays of ICG signals provided by some ICG systems. Experienced healthcare professionals can easily recognize the systolic and diastolic segments of these visual displays in addition to other attributes such as amplitude, shape, tone, slope and timing, in combination with hemodynamic parameters. Analysis of these attributes allows experienced healthcare professionals to ascertain an underlying disease state. However, variations in ICG signal attributes makes non-automated diagnosis difficult.
It is also known that some ICG systems provide minimal waveform information. When using these types of systems, healthcare professionals must rely largely on numeric parameters to make a diagnosis because these systems do not provide other information. With ICG systems that do not display waveforms, even experienced healthcare professionals may be unable to make a diagnosis. Based on the foregoing, there exists a need for an automated cardiography method and system for measuring cardiovasculograms that provides suggested underlying conditions based on correlating the recognized waveform attributes and hemodynamic parameters with waveform attributes and hemodynamic parameters associated with particular underlying conditions.